rRMSAnalyzer

ℹ️ Summary of input data

Project name : name

Data aligned on: GRCh38 (gencode annotation V46).
Analysed by: Allyson Moureaux
Date : 2025-09-08 09:05:00.159951

RiboClass Name : ribo_adj_annot_comp1
Number of uploaded samples: 12 samples
RNA(s) used by the coverage tool: NR_023363.1_5S, NR_046235.3_5.8S, NR_046235.3_18S, NR_046235.3_28S
There are 7217 genomic positions.

C-score
Method of computation : median
Window length : 6 nucleotides
There are 112 genomic positions with a valid C-score in all samples.
There are 112 annotated rRNA 2’Ome sites.

ComBat-seq
ComBat-seq correction: No
Metadata column used for ComBat-seq : None

1 Introduction

1.1 Content

The purpose of this analytical report (2Ome_profile) is to evaluate differences in rRNA 2’-O-methylation (2’Ome) levels between conditions using C-score values computed across 112 annotated rRNA sites. The objectives are:

- to provide a descriptive overview of methylation profiles by condition prior to formal testing;
- to determine whether methylation levels at individual sites differ significantly between experimental conditions;
- to perform pairwise comparisons between conditions using independent statistical tests.

1.2 Project summary

This 2’Ome_diff report is generated from the analysis of the project name, which is loaded into the RiboClass object ribo_adj_annot_comp1. The dataset used for the downstream analyses corresponds to a cohort of 12 biological samples, as part of the full dataset. This project was aligned on GRCh38 (gencode annotation V46).
. Each sample is annotated with associated metadata describing its biological and technical context.

These metadata are essential for interpreting the observed difference in 2’Ome level and for identifying potential technical biases, such as batch effects, or biological variations.

The following metadata are used in the project name:

1.3 Method: RNA 2’Ome interpretation and annotation

The 2’O-methylation (2’Ome) dataset consists of C-score values measured at 112 annotated rRNA sites for each biological sample. The C-score represents the level of 2’Ome of a given site in a specific sample, ranging from 0 (unmethylated) to 1 (fully methylated). Intermediate values reflect heterogenous methylation (i.e., heterogeneous methylation states across ribosomes within the same cell or across the cells of the sample). The method used to compute the C-score is detailed in the QC report (section 1.3 “Method”) and to extract the sites of interest in the 2’Ome_profile report (section 1.3 “Method: RNA 2’Ome interpretation and annotationrRNA”). These C-scores form the basis for all subsequent analyses, including global profiling and differential analysis.

Here is a table, which summarizes the annotated rRNA 2’Ome sites used in the downstream analyses where:
- “rna” column indicates the RNA molecule on which the 2’Ome site is located
- “rnapos” column indicates the position of the rRNA 2’Ome site
- “site” column indicates the name of the rRNA 2’Ome site by combining the RNA molecule on which it is located with the specific site identifier

1.4 Design of comparisons

Comparisons were designed to evaluate the differential levels of 2’Ome at each of the 112 annotated sites between biological conditions, as defined by the comp1 metadata field in the name project dataset. Group comparisons are performed when the dataset includes at least 2 conditions.

(1) Multiple comparisons are performed when the dataset includes 3 or more conditions. In this case, all groups are considered together in each comparison, enabling the identification of sites with significant differences in 2’Ome levels in at least one group compared to the others.
(2) Pairwise comparisons are performed when the dataset includes 2 conditions. In this case, each comparison involves a case group and a control group, enabling the identification of sites with significant differences in 2’Ome levels:

Comparison
- cond2 vs. cond1: 6 samples in the cond2 (case) group vs. 6 samples in the cond1 (control) group

These comparisons were designed to detect significant differences in site-specific 2’Ome levels between the control condition and each test condition. Both parametric and non-parametric statistical tests were applied independently to each site within each comparison, and adjusted p-values were computed to avoid bias due to the repetition of statistical tests (see section 3 for details).

2 Descriptive analysis of all annotated sites

In this first analysis section, the alteration in 2’Ome levels between the biological conditions are assessed globally, across all the 112 2’Ome sites.

2.1 C-score profiles by condition

The line plot shows the mean C-score for each site, ordered by genomic coordinates and stratified by biological condition. Shaded areas represent standard deviation, illustrating intra-group variability. Line colors correspond to biological groups. This figure highlights global 2’Ome profile trends and reveals sites with condition-specific variation.

2.2 Median C-score differences between conditions

A comparison of median C-scores between biological conditions was performed for the 112 annotated rRNA 2’Ome sites.

The left panel shows an histogram of ΔC-scores, calculated as ΔC-score = median C-score_case – median C-score_control. Each bar represents the ΔC-score at one site. Colors of the bar indicate the direction and magnitude of the difference at a specific site:

- red: substantial increase in the case group compared to the control group (|ΔC-score| > 0.05)
- blue: substantial decrease in the case group compared to the control group (|ΔC-score| > 0.05)
- gray: negligible changes (|ΔC-score| ≤ 0.05)

Of note : those differences are row differences and are not tested statistically yet. (See Section 3) for statistically significant results.

The right panel shows a scatter plot of median C-scores per site for each condition. Colors of the dots and horizontal lines indicate:
- dots: black: control group; orange: case group

- horizontal lines, which link dots when |ΔC-score| > 0.05, highlighting potentially meaningful site-specific changes: red: increase; blue: decrease

2.2.1 cond2 - cond1

3 Differential analysis of 2’Ome

3.1 Statistical testing strategy

To assess the differential level in 2′Ome at each of the 112 annotated sites, both parametric and non-parametric statistical tests were performed, depending on the number of biological conditions and the assumptions about the data distribution.

  1. Parametric Tests
  • In multiple-group comparisons involving three or more groups, a one-way ANOVA was applied to detect differences in mean C-scores across conditions. This method assumes that values are approximately normally distributed and that group variances are homogeneous (equal).
  • In two-group comparisons, the Welch’s t-test was used. This test compares means without assuming equal variances between groups.

  1. Non-Parametric Tests
    To complement the parametric approach, a non-parametric approach is performed.
  • For multiple-group comparisons, the Kruskal–Wallis test was performed across all biological conditions. As a non-parametric alternative to ANOVA, it does not assume normal distribution and is more stringent in detecting differences in distributions rather than means.
  • For two-group comparisons, the Wilcoxon rank-sum test (Mann–Whitney U test) was used in place of Welch’s test under non-parametric assumptions.

    All resulting p-values were corrected for multiple testing using the False Discovery Rate (FDR) procedure.

    Applying both parametric and non-parametric methods to the same dataset ensures greater robustness of the findings. Parametric tests are sensitive under ideal distributional conditions, while non-parametric tests offer a more conservative alternative when those assumptions may not be fully met. Sites identified as significant by both approaches are interpreted as the most reliable candidates for biological interpretation.

3.2 Parametric test

3.2.1 Multiple Conditions: ANOVA test

!! Note: This section is not applicable to the current dataset, which includes only two biological groups. In such cases, Welch’s t-test is the more appropriate parametric method for pairwise comparisons. Please refer to Section 3.2.2 Using Welch’s Test for the corresponding results.

3.2.2 Two conditions: Welch test

In this section, pairwise comparisons are performed between two biological conditions using Welch’s t-test, which is well-suited for assessing differences in group means without assuming equal variances.

Only rRNA 2′Ome sites meeting the criteria of |ΔC-score| > 0.05 (ΔC-score = Mean C-score_case – Mean C-score_control) and p < 0.05 (adjusted using the False Discovery Rate (FDR)) are considered significantly differentially methylated. A summary table of the metrics resulting from the Welch test (Case vs Control) is provided below, where:

- “annotated_sites”: name of the rRNA 2’Ome site
- “p_value”: raw p-value from the Welch’s t-test.
- “delta_c_score”: difference in mean C-scores between the two conditions
- “p_adj”: p-value adjusted for multiple comparisons using the False Discovery Rate (FDR) method.

cond2 vs cond1

This panel displays boxplots of C-scores for rRNA 2'Ome sites identified as significantly different between the two biological conditions cond2 and cond1, based on Welch's t-test results. Significance was defined by two criteria: adjusted p-value < 0.05 and |deltaC-score| > 0.05. Each boxplot shows the distribution of C-scores per condition, based on the summary table presented above. Colors are attributed to each biological group, enabling visual comparison of 2'Ome levels between conditions.

3.3 Non parametric tests

3.3.1 Multiple conditions: Kruskal–Wallis test

!! Note: This section is not applicable to the current dataset, which includes only two biological groups. The Kruskal–Wallis test is designed for comparing three or more groups. In cases involving two groups, the Wilcoxon rank-sum test (also known as the Mann–Whitney U test) is a more appropriate non-parametric alternative. Please refer to Section 3.3.2: Two conditions: Wilcoxon rank-sum test for the relevant results.

3.3.2 Two conditions: Wilcoxon rank-sum test

In this section, the Wilcoxon rank-sum test (also known as the Mann–Whitney U test) is applied to each rRNA 2′Ome site to assess whether the C-score distributions differ significantly between two biological conditions. This non-parametric test does not require assumptions of normality or equal variances, making it suitable for robust pairwise comparisons.

All p-values are corrected for multiple testing using the False Discovery Rate (FDR) method. Sites with an adjusted p-value < 0.05 and |ΔC-score| > 0.05 are considered significantly differentially methylated between the two conditions.

A summary table of the metrics resulting from the Wilcoxon rank-sum test (Case vs Control) is provided below, where:

- “annotated_sites”: name of the rRNA 2’Ome site
- “p_value”: raw p-value from the Wilcoxon rank-sum test.
- “delta_c_score”: difference in mean C-scores between the two conditions
- “p_adj”: p-value adjusted for multiple comparisons using the False Discovery Rate (FDR) method.

cond2 vs cond1

No significantly differentially methylated rRNA 2'O-methylation (2'Ome) sites were identified under the current threshold (Wilcoxon rank-sum test, adjusted p-value < 0.05 & |deltaC-score| > 0.05).

4 Conclusion

4.1 Analytic Synthesis

This diff_site report presents the analysis of 12 biological samples across 112 annotated rRNA 2’Ome sites. The initial analysis included Welch and Wilcoxon rank-sum tests, both of which were performed using all sites.
Statistical analyses were performed to assess differential rRNA 2′Ome site-specific variations across biological conditions. A dual approach combining parametric and non-parametric methods was adopted:
- For comparisons involving two biological groups, Welch’s t-test and the Wilcoxon rank-sum test were applied.
- For comparisons involving three or more groups, a one-way ANOVA and a Kruskal–Wallis test were used.

All resulting p-values were adjusted for multiple testing using the False Discovery Rate (FDR) procedure. Sites meeting the significance threshold (adjusted p-value < 0.05) and |ΔC-score| > 0.05 (when applicable) were retained.

> Summary of Differentially Methylated rRNA 2′Ome Sites > (Only significant sites in at least one test are shown)

4.2 Comments

No comments given

5 Additional information

5.1 Bibliography

  • Jaafar M, Paraqindes H, Gabut M, Diaz JJ, Marcel V*#, Durand S*#. 2’O-ribose methylation of ribosomal RNAs: natural diversity in living organisms, biological processes, and diseases. Cells 2021, 10(8):1948. https://doi.org/10.3390/cells10081948

  • Marcel V#, Kielbassa J, Marchand V, Natchiar SK, Paraqindes H, Nguyen Van Long F, Ayadi L, Bourguignon-Igel V, Lo Monaco P, Monchiet D, Scott V, Tonon L, Bray SE, Diot A, Jordan LB, Thompson AM, Bourdon JC, Dubois T, André F, Catez F, Puisieux A, Motorin Y, Klaholz BP, Viari A, Diaz JJ. Ribosomal RNA 2’O-methylation as novel layer of inter-tumour heterogeneity in breast cancer. NAR Cancer 2020, 2(4):zcaa036. https://doi.org/10.1093/narcan/zcab006

  • Paraqindes H*, Mourksi NEH*, Ballesta S*, Hedjam J, Bourdelais F, Fenouil T, Picart T, Catez F, Combe T, Ferrari A, Kielbassa J, Thomas E, Tonon L, Viari A, Attignon V, Carrere M, Perrossier J, Giraud S, Vanbelle C, Gabut M, Bergeron D, Scott MS, Castro Vega L, Magne N, Huillard E, Sanson M, Meyronet D, Diaz JJ, Ducray F#, Marcel V*#, Durand S*#. IDHwt and IDHmut adult-type diffuse gliomas display distinct alterations in ribosome biogenesis and 2’O-methylation of ribosomal RNA. Neuro Oncol 2023, 25(12):2191-2206. https://doi.org/10.1093/neuonc/noad140

5.2 Citation

When using this report, please cite: Paraqindes et al, Neuro Oncol 2023

5.3 License

rRMSAnalyzer
Copyright (C) 2023 Centre de Recherche en Cancérologie de Lyon

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

5.4 Session info

sessionInfo()
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C           LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] htmltools_0.5.8.1  glue_1.8.0         dplyr_1.1.4        kableExtra_1.4.0  
## [5] DT_0.34.0          rRMSAnalyzer_3.0.0
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3      rstudioapi_0.17.1       jsonlite_2.0.0         
##   [4] shape_1.4.6.1           magrittr_2.0.3          farver_2.1.2           
##   [7] rmarkdown_2.29          GlobalOptions_0.1.2     fs_1.6.6               
##  [10] ragg_1.5.0              vctrs_0.6.5             memoise_2.0.1          
##  [13] askpass_1.2.1           rstatix_0.7.2           curl_7.0.0             
##  [16] broom_1.0.9             Formula_1.2-5           sass_0.4.10            
##  [19] bslib_0.9.0             htmlwidgets_1.6.4       desc_1.4.3             
##  [22] fontawesome_0.5.3       plyr_1.8.9              httr2_1.2.1            
##  [25] cachem_1.1.0            whisker_0.4.1           lifecycle_1.0.4        
##  [28] iterators_1.0.14        pkgconfig_2.0.3         Matrix_1.7-3           
##  [31] R6_2.6.1                fastmap_1.2.0           GenomeInfoDbData_1.2.14
##  [34] MatrixGenerics_1.20.0   clue_0.3-66             digest_0.6.37          
##  [37] colorspace_2.1-1        patchwork_1.3.2         AnnotationDbi_1.70.0   
##  [40] S4Vectors_0.46.0        crosstalk_1.2.2         textshaping_1.0.3      
##  [43] RSQLite_2.4.3           ggpubr_0.6.1            labeling_0.4.3         
##  [46] fansi_1.0.6             httr_1.4.7              abind_1.4-8            
##  [49] mgcv_1.9-3              compiler_4.5.1          bit64_4.6.0-1          
##  [52] withr_3.0.2             doParallel_1.0.17       backports_1.5.0        
##  [55] BiocParallel_1.42.1     carData_3.0-5           DBI_1.2.3              
##  [58] ggsignif_0.6.4          MASS_7.3-65             openssl_2.3.3          
##  [61] rappdirs_0.3.3          rjson_0.2.23            tools_4.5.1            
##  [64] nlme_3.1-168            grid_4.5.1              cluster_2.1.8.1        
##  [67] reshape2_1.4.4          ade4_1.7-23             generics_0.1.4         
##  [70] sva_3.56.0              gtable_0.3.6            tidyr_1.3.1            
##  [73] xml2_1.4.0              car_3.1-3               XVector_0.48.0         
##  [76] BiocGenerics_0.54.0     ggrepel_0.9.6           foreach_1.5.2          
##  [79] pillar_1.11.0           stringr_1.5.1           limma_3.64.3           
##  [82] genefilter_1.90.0       circlize_0.4.16         splines_4.5.1          
##  [85] lattice_0.22-7          survival_3.8-3          bit_4.6.0              
##  [88] annotate_1.86.1         tidyselect_1.2.1        ComplexHeatmap_2.24.1  
##  [91] locfit_1.5-9.12         Biostrings_2.76.0       downlit_0.4.4          
##  [94] knitr_1.50              IRanges_2.42.0          edgeR_4.6.3            
##  [97] svglite_2.2.1           stats4_4.5.1            xfun_0.53              
## [100] Biobase_2.68.0          statmod_1.5.0           factoextra_1.0.7       
## [103] matrixStats_1.5.0       stringi_1.8.7           UCSC.utils_1.4.0       
## [106] yaml_2.3.10             evaluate_1.0.5          codetools_0.2-20       
## [109] tibble_3.3.0            colorRamp2_0.1.0        cli_3.6.5              
## [112] xtable_1.8-4            systemfonts_1.2.3       jquerylib_0.1.4        
## [115] Rcpp_1.1.0              GenomeInfoDb_1.44.2     png_0.1-8              
## [118] XML_3.99-0.19           parallel_4.5.1          pkgdown_2.1.3          
## [121] ggplot2_3.5.2           blob_1.2.4              viridisLite_0.4.2      
## [124] scales_1.4.0            purrr_1.1.0             crayon_1.5.3           
## [127] GetoptLong_1.0.5        rlang_1.1.6             cowplot_1.2.0          
## [130] KEGGREST_1.48.1

This report has been automatically generated with rRMSAnalyzer 2.0.1 (July 2025)